






'''simple docstring'''







from ..utils import DummyObject, requires_backends




class   __SCREAMING_SNAKE_CASE       (metaclass=__A       ):


  """simple docstring"""


  _a  : Any      			=     ['''flax''']







  def __init__(			self   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         requires_backends(self   ,	['flax']	)







  @classmethod
  def    _a       (			cls   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         requires_backends(cls   ,	['flax']	)







  @classmethod
  def    _a       (			cls   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         requires_backends(cls   ,	['flax']	)





class   __SCREAMING_SNAKE_CASE       (metaclass=__A       ):


  """simple docstring"""


  _a  : Optional[int]      			=     ['''flax''']







  def __init__(			self   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         requires_backends(self   ,	['flax']	)







  @classmethod
  def    _a       (			cls   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         requires_backends(cls   ,	['flax']	)







  @classmethod
  def    _a       (			cls   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         requires_backends(cls   ,	['flax']	)





class   __SCREAMING_SNAKE_CASE       (metaclass=__A       ):


  """simple docstring"""


  _a  : Tuple      			=     ['''flax''']







  def __init__(			self   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         requires_backends(self   ,	['flax']	)







  @classmethod
  def    _a       (			cls   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         requires_backends(cls   ,	['flax']	)







  @classmethod
  def    _a       (			cls   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         requires_backends(cls   ,	['flax']	)





class   __SCREAMING_SNAKE_CASE       (metaclass=__A       ):


  """simple docstring"""


  _a  : Dict      			=     ['''flax''']







  def __init__(			self   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         requires_backends(self   ,	['flax']	)







  @classmethod
  def    _a       (			cls   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         requires_backends(cls   ,	['flax']	)







  @classmethod
  def    _a       (			cls   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         requires_backends(cls   ,	['flax']	)





class   __SCREAMING_SNAKE_CASE       (metaclass=__A       ):


  """simple docstring"""


  _a  : List[str]      			=     ['''flax''']







  def __init__(			self   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         requires_backends(self   ,	['flax']	)







  @classmethod
  def    _a       (			cls   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         requires_backends(cls   ,	['flax']	)







  @classmethod
  def    _a       (			cls   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         requires_backends(cls   ,	['flax']	)





class   __SCREAMING_SNAKE_CASE       (metaclass=__A       ):


  """simple docstring"""


  _a  : Optional[int]      			=     ['''flax''']







  def __init__(			self   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         requires_backends(self   ,	['flax']	)







  @classmethod
  def    _a       (			cls   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         requires_backends(cls   ,	['flax']	)







  @classmethod
  def    _a       (			cls   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         requires_backends(cls   ,	['flax']	)





class   __SCREAMING_SNAKE_CASE       (metaclass=__A       ):


  """simple docstring"""


  _a  : Dict      			=     ['''flax''']







  def __init__(			self   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         requires_backends(self   ,	['flax']	)







  @classmethod
  def    _a       (			cls   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         requires_backends(cls   ,	['flax']	)







  @classmethod
  def    _a       (			cls   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         requires_backends(cls   ,	['flax']	)





class   __SCREAMING_SNAKE_CASE       (metaclass=__A       ):


  """simple docstring"""


  _a  : Optional[Any]      			=     ['''flax''']







  def __init__(			self   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         requires_backends(self   ,	['flax']	)







  @classmethod
  def    _a       (			cls   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         requires_backends(cls   ,	['flax']	)







  @classmethod
  def    _a       (			cls   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         requires_backends(cls   ,	['flax']	)





class   __SCREAMING_SNAKE_CASE       (metaclass=__A       ):


  """simple docstring"""


  _a  : List[Any]      			=     ['''flax''']







  def __init__(			self   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         requires_backends(self   ,	['flax']	)







  @classmethod
  def    _a       (			cls   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         requires_backends(cls   ,	['flax']	)







  @classmethod
  def    _a       (			cls   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         requires_backends(cls   ,	['flax']	)





class   __SCREAMING_SNAKE_CASE       (metaclass=__A       ):


  """simple docstring"""


  _a  : int      			=     ['''flax''']







  def __init__(			self   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         requires_backends(self   ,	['flax']	)







  @classmethod
  def    _a       (			cls   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         requires_backends(cls   ,	['flax']	)







  @classmethod
  def    _a       (			cls   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         requires_backends(cls   ,	['flax']	)





class   __SCREAMING_SNAKE_CASE       (metaclass=__A       ):


  """simple docstring"""


  _a  : Any      			=     ['''flax''']







  def __init__(			self   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         requires_backends(self   ,	['flax']	)







  @classmethod
  def    _a       (			cls   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         requires_backends(cls   ,	['flax']	)







  @classmethod
  def    _a       (			cls   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         requires_backends(cls   ,	['flax']	)





class   __SCREAMING_SNAKE_CASE       (metaclass=__A       ):


  """simple docstring"""


  _a  : int      			=     ['''flax''']







  def __init__(			self   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         requires_backends(self   ,	['flax']	)







  @classmethod
  def    _a       (			cls   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         requires_backends(cls   ,	['flax']	)







  @classmethod
  def    _a       (			cls   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         requires_backends(cls   ,	['flax']	)





class   __SCREAMING_SNAKE_CASE       (metaclass=__A       ):


  """simple docstring"""


  _a  : Dict      			=     ['''flax''']







  def __init__(			self   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         requires_backends(self   ,	['flax']	)







  @classmethod
  def    _a       (			cls   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         requires_backends(cls   ,	['flax']	)







  @classmethod
  def    _a       (			cls   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         requires_backends(cls   ,	['flax']	)












'''simple docstring'''







import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class   __SCREAMING_SNAKE_CASE       (__A							,       __A							,       unittest.TestCase       ):


  """simple docstring"""


  _a  : str      			=     CycleDiffusionPipeline
  _a  : int      			=     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
      '''negative_prompt''',
      '''height''',
      '''width''',
      '''negative_prompt_embeds''',
  }
  _a  : str      			=     PipelineTesterMixin.required_optional_params - {'''latents'''}
  _a  : str      			=     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''source_prompt'''}       )
  _a  : Optional[Any]      			=     IMAGE_TO_IMAGE_IMAGE_PARAMS
  _a  : Optional[int]      			=     IMAGE_TO_IMAGE_IMAGE_PARAMS







  def    _a       (			self	):






         """simple docstring"""


         torch.manual_seed(0	)
         a_						     =			UNetaDConditionModel(
             block_out_channels=(32, 64)   ,	layers_per_block=2   ,	sample_size=32   ,	in_channels=4   ,	out_channels=4   ,	down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')   ,	up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')   ,	cross_attention_dim=32   ,	)
         a_						     =			DDIMScheduler(
             beta_start=0.00_085   ,	beta_end=0.012   ,	beta_schedule='scaled_linear'   ,	num_train_timesteps=1_000   ,	clip_sample=UpperCamelCase__   ,	set_alpha_to_one=UpperCamelCase__   ,	)
         torch.manual_seed(0	)
         a_						     =			AutoencoderKL(
             block_out_channels=[32, 64]   ,	in_channels=3   ,	out_channels=3   ,	down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']   ,	up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']   ,	latent_channels=4   ,	)
         torch.manual_seed(0	)
         a_						     =			CLIPTextConfig(
             bos_token_id=0   ,	eos_token_id=2   ,	hidden_size=32   ,	intermediate_size=37   ,	layer_norm_eps=1e-05   ,	num_attention_heads=4   ,	num_hidden_layers=5   ,	pad_token_id=1   ,	vocab_size=1_000   ,	)
         a_						     =			CLIPTextModel(UpperCamelCase__	)
         a_						     =			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

         a_						     =			{
             'unet': unet,
             'scheduler': scheduler,
             'vae': vae,
             'text_encoder': text_encoder,
             'tokenizer': tokenizer,
             'safety_checker': None,
             'feature_extractor': None,
         }
         return components







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__=0	):






         """simple docstring"""


         a_						     =			floats_tensor((1, 3, 32, 32)   ,	rng=random.Random(UpperCamelCase__	)	).to(UpperCamelCase__	)
         a_						     =			image / 2 + 0.5
         if str(UpperCamelCase__	).startswith('mps'	):
                a_						     =			torch.manual_seed(UpperCamelCase__	)
         else:
                a_						     =			torch.Generator(device=UpperCamelCase__	).manual_seed(UpperCamelCase__	)
         a_						     =			{
             'prompt': 'An astronaut riding an elephant',
             'source_prompt': 'An astronaut riding a horse',
             'image': image,
             'generator': generator,
             'num_inference_steps': 2,
             'eta': 0.1,
             'strength': 0.8,
             'guidance_scale': 3,
             'source_guidance_scale': 1,
             'output_type': 'numpy',
         }
         return inputs







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			'cpu'  # ensure determinism for the device-dependent torch.Generator

         a_						     =			self.get_dummy_components()
         a_						     =			CycleDiffusionPipeline(**UpperCamelCase__	)
         a_						     =			pipe.to(UpperCamelCase__	)
         pipe.set_progress_bar_config(disable=UpperCamelCase__	)

         a_						     =			self.get_dummy_inputs(UpperCamelCase__	)
         a_						     =			pipe(**UpperCamelCase__	)
         a_						     =			output.images

         a_						     =			images[0, -3:, -3:, -1]

         assert images.shape == (1, 32, 32, 3)
         a_						     =			np.array([0.4_459, 0.4_943, 0.4_544, 0.6_643, 0.5_474, 0.4_327, 0.5_701, 0.5_959, 0.5_179]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2







  @unittest.skipIf(torch_device != 'cuda'   ,	'This test requires a GPU'	)
  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.get_dummy_components()
         for name, module in components.items():
                if hasattr(UpperCamelCase__   ,	'half'	):
                       a_						     =			module.half()
         a_						     =			CycleDiffusionPipeline(**UpperCamelCase__	)
         a_						     =			pipe.to(UpperCamelCase__	)
         pipe.set_progress_bar_config(disable=UpperCamelCase__	)

         a_						     =			self.get_dummy_inputs(UpperCamelCase__	)
         a_						     =			pipe(**UpperCamelCase__	)
         a_						     =			output.images

         a_						     =			images[0, -3:, -3:, -1]

         assert images.shape == (1, 32, 32, 3)
         a_						     =			np.array([0.3_506, 0.4_543, 0.446, 0.4_575, 0.5_195, 0.4_155, 0.5_273, 0.518, 0.4_116]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2







  @skip_mps
  def    _a       (			self	):






         """simple docstring"""


         return super().test_save_load_local()







  @unittest.skip('non-deterministic pipeline'	)
  def    _a       (			self	):






         """simple docstring"""


         return super().test_inference_batch_single_identical()







  @skip_mps
  def    _a       (			self	):






         """simple docstring"""


         return super().test_dict_tuple_outputs_equivalent()







  @skip_mps
  def    _a       (			self	):






         """simple docstring"""


         return super().test_save_load_optional_components()







  @skip_mps
  def    _a       (			self	):






         """simple docstring"""


         return super().test_attention_slicing_forward_pass()





@slow
@require_torch_gpu
class   __SCREAMING_SNAKE_CASE       (unittest.TestCase       ):


  """simple docstring"""


  def    _a       (			self	):






         """simple docstring"""


         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/cycle-diffusion/black_colored_car.png'	)
         a_						     =			load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'	)
         a_						     =			init_image.resize((512, 512)	)

         a_						     =			'CompVis/stable-diffusion-v1-4'
         a_						     =			DDIMScheduler.from_pretrained(UpperCamelCase__   ,	subfolder='scheduler'	)
         a_						     =			CycleDiffusionPipeline.from_pretrained(
             UpperCamelCase__   ,	scheduler=UpperCamelCase__   ,	safety_checker=UpperCamelCase__   ,	torch_dtype=torch.floataa   ,	revision='fp16'	)

         pipe.to(UpperCamelCase__	)
         pipe.set_progress_bar_config(disable=UpperCamelCase__	)
         pipe.enable_attention_slicing()

         a_						     =			'A black colored car'
         a_						     =			'A blue colored car'

         a_						     =			torch.manual_seed(0	)
         a_						     =			pipe(
             prompt=UpperCamelCase__   ,	source_prompt=UpperCamelCase__   ,	image=UpperCamelCase__   ,	num_inference_steps=100   ,	eta=0.1   ,	strength=0.85   ,	guidance_scale=3   ,	source_guidance_scale=1   ,	generator=UpperCamelCase__   ,	output_type='np'   ,	)
         a_						     =			output.images

         # the values aren't exactly equal, but the images look the same visually
         assert np.abs(image - expected_image	).max() < 5e-1







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/cycle-diffusion/black_colored_car.png'	)
         a_						     =			load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'	)
         a_						     =			init_image.resize((512, 512)	)

         a_						     =			'CompVis/stable-diffusion-v1-4'
         a_						     =			DDIMScheduler.from_pretrained(UpperCamelCase__   ,	subfolder='scheduler'	)
         a_						     =			CycleDiffusionPipeline.from_pretrained(UpperCamelCase__   ,	scheduler=UpperCamelCase__   ,	safety_checker=UpperCamelCase__	)

         pipe.to(UpperCamelCase__	)
         pipe.set_progress_bar_config(disable=UpperCamelCase__	)
         pipe.enable_attention_slicing()

         a_						     =			'A black colored car'
         a_						     =			'A blue colored car'

         a_						     =			torch.manual_seed(0	)
         a_						     =			pipe(
             prompt=UpperCamelCase__   ,	source_prompt=UpperCamelCase__   ,	image=UpperCamelCase__   ,	num_inference_steps=100   ,	eta=0.1   ,	strength=0.85   ,	guidance_scale=3   ,	source_guidance_scale=1   ,	generator=UpperCamelCase__   ,	output_type='np'   ,	)
         a_						     =			output.images

         assert np.abs(image - expected_image	).max() < 2e-2




